Principal Engineer – AI/ML Analytics Platform & Cloud Security

Netskope is a leading cloud security company focused on redefining Cloud, Network, and Data Security. They are seeking a Principal Engineer to architect and lead the development of an advanced AI-powered analytics platform that enhances cloud security intelligence through machine learning and real-time insights.

Responsibilities:

• Define and drive the architecture for an AI analytics platform that supports natural language queries, visual analytics, and ML-assisted insights across security data
• Lead the integration of LLMs and Retrieval-Augmented Generation (RAG) into interactive analytics flows, enabling context-rich user experiences
• Own the design and development of high-performance data systems for querying, indexing, and streaming large-scale telemetry and behavioral data
• Drive backend platform scalability, availability, and observability across core analytics and ML services
• Partner with security, data science, and product teams to prioritize use cases, define technical strategy, and influence roadmap
• Establish engineering best practices in system design, API architecture, performance tuning, data modeling, and ML platform integration
• Mentor senior engineers and foster a high-bar engineering culture grounded in innovation, ownership, and execution
• Represent the engineering vision in cross-functional strategy discussions, architectural reviews, and external technical forums if needed

Requirements:

• 15+ years of experience building scalable, distributed systems for data analytics, ML, or search-based platforms
• Proven track record of architecting and delivering end-to-end AI or analytics platforms (BI tools, data apps, or ML-driven insights platforms)
• Deep expertise in backend engineering using Python, Java, or Scala; advanced proficiency in SQL and performance optimization
• Experience designing streaming and batch data pipelines using tools like Spark, Kafka, Flink, or equivalent
• Hands-on experience with MLOps platforms and modern ML deployment workflows (e.g., MLflow, Kubeflow, Airflow)
• Strong understanding of LLMs and vector databases (e.g., Pinecone, PGVector) and their application in semantic search and insight generation
• Deep understanding of data modeling for analytical systems (star/snowflake schemas, OLAP, dimensional modeling)
• Demonstrated success in building platforms that power user-facing experiences like dashboards, alerts, or search interfaces (e.g., ThoughtSpot, Looker, or similar)
• Experience working with modern cloud platforms (AWS, GCP, Azure) and big data storage engines (BigQuery, ClickHouse, Snowflake)
• Proven ability to balance technical depth with product intuition—able to align platform direction with user value and business goals
• Exceptional communication and collaboration skills across functions—engineering, product, data science, and executive stakeholders
• Ability to define and influence architectural direction at an organizational level
• Experience mentoring staff- and senior-level engineers and setting long-term engineering strategies
• Prior experience in security analytics, threat detection, or operationalizing security data at scale
• Exposure to natural language query systems or AI copilots (e.g., NL2SQL, prompt engineering, question-answering)
• Contributions to open-source platforms in analytics, AI infrastructure, or LLM tools